| """Unit tests for src.utils.random_utils -- the determinism backbone of the whole pipeline.""" |
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| from __future__ import annotations |
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| import numpy as np |
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| from src.config.schema import Range |
| from src.utils.random_utils import SeedManager, derive_seed, resolve_scalar_or_range |
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| def test_derive_seed_is_deterministic(): |
| assert derive_seed(42, 1, "placement") == derive_seed(42, 1, "placement") |
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| def test_derive_seed_varies_with_each_part(): |
| base = derive_seed(42, 1, "placement") |
| assert derive_seed(42, 2, "placement") != base |
| assert derive_seed(42, 1, "rendering") != base |
| assert derive_seed(43, 1, "placement") != base |
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| def test_derive_seed_in_valid_range(): |
| seed = derive_seed(0, 999999, "x") |
| assert 0 <= seed < 2**32 |
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| def test_seed_manager_same_sample_same_stage_is_deterministic(): |
| sm1 = SeedManager(master_seed=7, sample_index=3) |
| sm2 = SeedManager(master_seed=7, sample_index=3) |
| draws1 = sm1.numpy_rng("placement").uniform(0, 1, size=10) |
| draws2 = sm2.numpy_rng("placement").uniform(0, 1, size=10) |
| assert np.allclose(draws1, draws2) |
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| def test_seed_manager_different_stages_are_independent(): |
| sm = SeedManager(master_seed=7, sample_index=3) |
| a = sm.numpy_rng("placement").uniform(0, 1, size=5) |
| b = sm.numpy_rng("rendering").uniform(0, 1, size=5) |
| assert not np.allclose(a, b) |
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| def test_seed_manager_different_samples_are_independent(): |
| a = SeedManager(master_seed=7, sample_index=1).numpy_rng("placement").uniform(0, 1, size=5) |
| b = SeedManager(master_seed=7, sample_index=2).numpy_rng("placement").uniform(0, 1, size=5) |
| assert not np.allclose(a, b) |
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| def test_resolve_scalar_or_range_with_scalar(): |
| rng = np.random.default_rng(0) |
| assert resolve_scalar_or_range(0.5, rng) == 0.5 |
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| def test_resolve_scalar_or_range_with_range_stays_in_bounds(): |
| rng = np.random.default_rng(0) |
| r = Range(min=-3.0, max=5.0) |
| for _ in range(200): |
| v = resolve_scalar_or_range(r, rng) |
| assert -3.0 <= v <= 5.0 |
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| def test_resolve_scalar_or_range_with_dict_range(): |
| rng = np.random.default_rng(0) |
| v = resolve_scalar_or_range({"min": 1.0, "max": 1.0}, rng) |
| assert v == 1.0 |
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| def test_resolve_scalar_or_range_degenerate_range_returns_bound(): |
| rng = np.random.default_rng(0) |
| r = Range(min=2.0, max=2.0) |
| assert resolve_scalar_or_range(r, rng) == 2.0 |
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